When most people think about AI in health, they imagine chatbots or SMS campaigns delivering timely nudges. What they rarely see is the invisible engine underneath: the GPUs powering it all. At DPE, our mission is bold – to deliver 10 billion health messages that reach households across Africa and beyond. To get there, we don’t just need smart algorithms. We need AI hardware and GPUs running at peak efficiency.

1. Clock Speed: The Heartbeat of Scale

Every GPU has a clock – its heartbeat. The faster it ticks, the more work gets done per second. For us, that means handling bursts of millions of SMS messages during vaccine campaigns or malaria interventions without delay.
Think of it like a community health promoter who can walk twice as fast between households, they simply reach more families in less time.

2. FLOPs: The Raw Muscle

FLOPs (floating-point operations per second) measure raw GPU muscle – how many math problems it can solve every second. Attention-based AI models (the brains behind InfoAFYA’s health messaging services) eat FLOPs for breakfast.
More FLOPs = more real-time conversations, more multi-language support, and more households served simultaneously.

3. Attention: When Scale Becomes Expensive

AI models rely on attention mechanisms to decide what information matters. But traditional (vanilla) attention is quadratic – the cost explodes as conversations get longer. For clinical notes, or millions of SMS messages, this is unsustainable.

• The Problem: Like a health worker trying to remember every single interaction with every family. Overwhelming.
• The Fix: Smarter techniques like Sliding Window Attention, KV Cache, and PagedAttention. These allow our system to “remember what matters” without burning through GPU memory. This comes out of the box with the LLaMa AI models we currently use in our architecture.
  This means multi-turn WhatsApp chats for chronic disease support are both affordable and scalable.

4. GPUs Up Close: Why Hardware Matters

• Kernels: The GPU’s “recipes” for math. Well-optimized kernels = faster campaigns generated.
• HBM (High Bandwidth Memory): The data highway. Wider highways = faster insights from Millions of health datapoints we already manage.
• Memory Hierarchy: Smarter memory use = more multi-county, multilingual campaigns delivered without spiraling compute costs.

Without these optimizations, scaling to 10B health messages would be financially impossible.

Why This Matters

Every clock cycle, FLOP, and memory optimization is about more than hardware. It’s about saving lives at scale. If GPUs run 2x more efficiently, that’s 2x more households receiving reminders to vaccinate, take malaria medication, or manage sickle cell care.

This is why at DPE, we obsess about GPU availability, design, and AI efficiency: because hardware choices ripple into real-world health outcomes. And this is why we are partnering with Cassava Technologies.

We’re building the rails for a future where every person can receive timely, personalized, and safe health nudges. To get there, GPUs aren’t just accelerators – they’re enablers of equity. The smarter we use them, the faster we deliver on our mission: 10 billion health messages, each one making care more accessible. Most people will never think about FLOPs or KV caches. And that’s fine. Because if we do our job right, what they’ll notice is a simple SMS arriving right when they need it most.